Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSpectroscopic classification of a complete sample of astrometrically-selected quasar candidates using Gaia DR2
408
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Here we explore the efficiency and fidelity of a purely astrometric selection of quasars as point sources with zero proper motions in the {it Gaia} data release 2 (DR2). We have built a complete candidate sample including 104 Gaia-DR2 point sources brighter than $G<20$ mag within one degree of the north Galactic pole (NGP), all with proper motions consistent with zero within 2$sigma$ uncertainty. In addition to pre-existing spectra, we have secured long-slit spectroscopy of all the remaining candidates and find that all 104 stationary point sources in the field can be classified as either quasars (63) or stars (41). The selection efficiency of the zero-proper-motion criterion at high Galactic latitudes is thus $approx 60%$. Based on this complete quasar sample we examine the basic properties of the underlying quasar population within the imposed limiting magnitude. We find that the surface density of quasars is 20 deg$^{-2}$, the redshift distribution peaks at $zsim1.5$, and that only eight systems ($13^{+5}_{-3}%$) show significant dust reddening. We then explore the selection efficiency of commonly used optical, near- and mid-infrared quasar identification techniques and find that they are all complete at the $85-90%$ level compared to the astrometric selection. Finally, we discuss how the astrometric selection can be improved to an efficiency of $approx70%$ by including an additional cut requiring parallaxes of the candidates to be consistent with zero within 2$sigma$. The selection efficiency will further increase with the release of future, more sensitive astrometric measurement from the Gaia mission. This type of selection, purely based on the astrometry of the quasar candidates, is unbiased in terms of colours and emission mechanisms of the quasars and thus provides the most complete census of the quasar population within the limiting magnitude of Gaia.
rate research
Read More
Context. Strong gravitationally lensed quasars are among the most interesting and useful observable extragalactic phenomena. Because their study constitutes a unique tool in various fields of astronomy, they are highly sought, not without difficulty. Indeed, even in this era of all-sky surveys, their recognition remains a great challenge, with barely a few hundred currently known systems. Aims. In this work we aim to detect new strongly lensed quasar candidates in the recently published Gaia Data Release 2 (DR2), which is the highest spatial resolution astrometric and photometric all-sky survey, attaining effective resolutions from 0.4 to 2.2. Methods. We cross-matched a merged list of quasars and candidates with the Gaia DR2 and found 1,839,143 counterparts within 0.5. We then searched matches with more than two Gaia DR2 counterparts within 6. We further narrowed the resulting list using astrometry and photometry compatibility criteria between the Gaia DR2 counterparts. A supervised machine learning method, Extremely Randomized Trees, is finally adopted to assign to each remaining system a probability of being lensed. Results. We report the discovery of three quadruply-imaged quasar candidates that are fully detected in Gaia DR2. These are the most promising new quasar lens candidates from Gaia DR2 and a simple singular isothermal ellipsoid lens model is able to reproduce their image positions to within $sim$1 mas. This letter demonstrates the gravitational lens discovery potential of Gaia.
Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost impossible to model. We tackle this problem with a novel approach, using convolutional neural networks (CNNs), a state-of-the-art image classification tool, for automatic classification of galaxy cluster candidates. We train the networks on combined XMM-Newton X-ray observations with their optical counterparts from the all-sky Digitized Sky Survey. Our data set originates from the X-CLASS survey sample of galaxy cluster candidates, selected by a specially developed pipeline, the XAmin, tailored for extended source detection and characterisation. Our data set contains 1 707 galaxy cluster candidates classified by experts. Additionally, we create an official Zooniverse citizen science project, The Hunt for Galaxy Clusters, to probe whether citizen volunteers could help in a challenging task of galaxy cluster visual confirmation. The project contained 1 600 galaxy cluster candidates in total of which 404 overlap with the experts sample. The networks were trained on expert and Zooniverse data separately. The CNN test sample contains 85 spectroscopically confirmed clusters and 85 non-clusters that appear in both data sets. Our custom network achieved the best performance in the binary classification of clusters and non-clusters, acquiring accuracy of 90 %, averaged after 10 runs. The results of using CNNs on combined X-ray and optical data for galaxy cluster candidate classification are encouraging and there is a lot of potential for future usage and improvements.
The combination of the final version of the RAVE spectroscopic survey data release 6 with radial velocities and astrometry from Gaia DR2 allows us to identify and create a catalog of single lined binary star candidates (SB1), their inferred orbital parameters, and to inspect possible double lined binary stars (SB2). A probability function for the detection of radial velocity (RV) variations is used for identifying SB1 candidates. The estimation of orbital parameters for main sequence dwarfs is performed by matching the measured RVs with theoretical velocity curves sampling the orbital parameter space. The method is verified by studying a mock sample from the SB9 catalogue. Studying the boxiness and asymmetry of the spectral lines allows us to identify possible SB2 candidates, while matching their spectra to a synthetic library indicates probable properties of their components. From the RAVE catalog we select 37,664 stars with multiple RV measurements and identify 3838 stars as SB1 candidates. Joining RAVE and Gaia DR2 yields 450,646 stars with RVs measured by both surveys and 27,716 of them turn out to be SB1 candidates, which is an increase by an order of magnitude over previous studies. For main sequence dwarf candidates we calculate their most probable orbital parameters: orbital periods are not longer than a few years and primary components have masses similar to the Solar mass. All our results are available via Vizier/CDS.
79 -
Coryn A.L. Bailer-Jones
, Morgan Fouesneau
, Rene Andrae (Max Planckn Institute for Astronomy
2019
We construct a supervised classifier based on Gaussian Mixture Models to probabilistically classify objects in Gaia data release 2 (GDR2) using only photometric and astrometric data in that release. The model is trained empirically to classify objects into three classes -- star, quasar, galaxy -- for G<=14.5 mag down to the Gaia magnitude limit of G=21.0 mag. Galaxies and quasars are identified for the training set by a cross-match to objects with spectroscopic classifications from the Sloan Digital Sky Survey. Stars are defined directly from GDR2. When allowing for the expectation that quasars are 500 times rarer than stars, and galaxies 7500 times rarer than stars (the class imbalance problem), samples classified with a threshold probability of 0.5 are predicted to have purities of 0.43 for quasars and 0.28 for galaxies, and completenesses of 0.58 and 0.72 respectively. The purities can be increased up to 0.60 by adopting a higher threshold. Not accounting for this expected low frequency of extragalactic objects (the class prior) would give both erroneously optimistic performance predictions and severely impure samples. Applying our model to all 1.20 billion objects in GDR2 with the required features, we classify 2.3 million objects as quasars and 0.37 million objects as galaxies (with individual probabilities above 0.5). The small number of galaxies is due to the strong bias of the satellite detection algorithm and on-ground data selection against extended objects. We infer the true number of quasars and galaxies -- as these classes are defined by our training set -- to be 690,000 and 110,000 respectively (+/- 50%). The aim of this work is to see how well extragalactic objects can be classified using only GDR2 data. Better classifications should be possible with the low resolution spectroscopy (BP/RP) planned for GDR3.
In Ostdiek et al. (2019), we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release of Gaia (DR2). In this paper, we apply two clustering algorithms to identify velocity substructure within this catalog. We focus on the subset of stars with line-of-sight velocity measurements that fall in the range of Galactocentric radii $r in [6.5, 9.5]$ kpc and vertical distances $|z| < 3$ kpc. Known structures such as Gaia Enceladus and the Helmi stream are identified. The largest previously-unknown structure, Nyx, first introduced in Necib et al. (2019a), is a vast stream consisting of at least 500 stars in the region of interest. This study displays the power of the machine learning approach by not only successfully identifying known features, but also discovering new kinematic structures that may shed light on the merger history of the Milky Way.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
